Welcome to the LEES Lab
The LEES Lab at the CGCEO of Michigan State University, directed by
Dr.Jiquan Chen, is interested in scientific investigations and education on
fundamental ecosystem and landscape processes for understanding ecosystem
functions and management.
Our current studies are focused on the carbon and water cycles of
different ecosystems (grassland, desert, forest, cropland, wetlands,
freshwater) at multiple spatial and
temporal scales, bioenergy systems and resource uses, coupled interactions
and feedback between climatic change and human activities, and sustainable
management and conservation.
Our research projects, spreading mostly across North American and
Asian landscapes, are based on sound field experiments and monitoring
stations, state-of-the-art equipment and technology, modeling, and remote
sensing technology. The LEES Lab is also the home of book series on
"Ecosystem Science and Applications—ESA" for the Higher Education Press
(HEP) and De Gruyter. We maintain a high ethical and liberal standard for professional collaborations in research and education.
Estimating stand volume and above-ground biomass of urban forests using LiDAR
Giannico, V., R. Lafortezza, R. John, G. Sanesi, L. Pesola, and J. Chen. 2016. Estimating stand volume and above-ground biomass of urban forests using LiDAR. Remote Sensing 8: 339; doi:10.3390/rs8040339
LiDAR profiles and 3D Views of a forested area of Parco Nord Milano (a); (b) and (c) are respectively the profile and 3D View of Plot 1A, 29 years old; (d) and (e) are respectively the profile and 3D View of Plot 23B, 17 years old.
Assessing forest stand conditions in urban and peri-urban areas is essential to support ecosystem service planning and management, as most of the ecosystem services provided are a consequence of forest stand characteristics. However, collecting data for assessing forest stand conditions is time consuming and labor intensive. A plausible approach for addressing this issue is to establish a relationship between in situ measurements of stand characteristics and data from airborne laser scanning (LiDAR).
In this study we assessed forest stand volume and above-ground biomass (AGB) in a broadleaved urban forest, using a combination of LiDAR-derived metrics, which takes the form of a forest allometric model. We tested various methods for extracting proxies of basal area (BA) and mean stand height (H) from the LiDAR point-cloud distribution and evaluated the performance of different models in estimating forest stand volume and AGB.